Recently, my colleagues and I published a scientific paper in the journal Algorithms on an algorithm that allows for fast and interactive positioning of proteins within membranes. The original model was strongly inspired by Brasseur's work from the end of the 90s.
Molecular simulations of protein alignment in membranes are crucial for understanding the behavior and function of these biological systems. However, traditional molecular simulation methods such as molecular dynamics simulations in fully hydrated lipid bilayers can be time-consuming and difficult to manipulate in real-time.
To address this challenge, we developed an algorithm suitable for Interactive Molecular Simulations (IMS) that allows for on-the-fly monitoring and manipulation of protein alignment in membranes at various scales. We integrated several tools, including UnityMol, MDDriver, and BioSpring, to create a flexible and user-friendly framework for IMS.
One key component of our IMS framework is the integration of an implicit membrane model based on the Integral Membrane Protein And Lipid Association (IMPALA) approach. This model allows for multiple levels of representation and the ability to tune degrees of freedom for optimal performance. We validated the IMPALA model in both interactive and exhaustive search modes to ensure its accuracy and reliability. This was not an easy task, because reproducibility for the implementation was difficult due to lacking information in the literature. We tried to reconstruct as good as possible the original conditions of the implementation.
This observation points to one of the challenges in reproducing computational methods from the scientific literature: the frequent lack of comprehensive information and access to source code. Without access to all the necessary details and resources, it is often difficult to fully understand and reproduce computational methods. Even when methods are described in detail in the paper, it can be difficult to understand the underlying implementation without access to the source code. As a result, it can be quite difficult to validate and build upon the methods described in the literature, which can limit their impact and usefulness.
In conclusion, our IMS algorithm allows for real-time, interactive positioning of proteins within membranes, providing a powerful interactive tool for studying the behavior and function of these complex biological systems. I am excited to share this work and hope that it will lead to new insights into the role of proteins in membranes and their impact on human health and disease.
This paper is published with reference André Lanrezac, Benoist Laurent, Hubert Santuz, Nicolas Férey, Marc Baaden. Fast and Interactive Positioning of Proteins within Membranes. Algorithms, 2022, 15 (11), pp.415.; the content is openly accessible from this website. Software and data are also available with doi:10.57745/NSHIWZ.
Managing a list of datasets as a researcher can be a bit of a challenge, especially when it comes to citing them properly. Unlike a bibliography of publications, there are currently very few tools available to help with this task. In these early days of data management, it can be a bit complicated to keep track of all the datasets you have produced, want to use or are interested in.
One approach to organizing your datasets is to collect their doi identifiers from publicly accessible repositories such as OpenAIRE. There is a convenient CSV export functionality, for instance. You can then use a code like the doiclient python tool contributed by Jonathan Barnoud to retrieve the metadata for this doi list. It uses the nice Crosscite citation formatter. From there, you can extract for instance a bibtex bibliography of all your datasets. With such a bibliography you can then use tools such as pybtex to format the metadata into markdown or html for inclusion on your website.
One potential difficulty you may encounter is with figshare, which is a popular platform for sharing datasets. Many datasets on figshare do not have their own doi, only the doi of the publication they refer to. This can make it difficult to properly process and cite these datasets.
It would be great if there were a data management software with a catalog similar to the ones we have for publications, such as Zotero, but more specific to data. By that I mean the ability for instance to dynamically update, in case there is a new version of the dataset, and also not duplicate different versions of a given dataset for instance. Unfortunately, such a tool does not seem to exist yet, but it would certainly be a welcome addition to the data management landscape.
In the meantime, it is important to do your best to properly cite and organize your datasets. If you have any feedback or suggestions on how to improve this process, please don't hesitate to share it. Here is a link to the new datasets page on my website, where you can see the results of my efforts.
In summary, I recommend to
collect and use doi identifiers for your datasets whenever possible. Maybe a few alternative identifiers such as figshare id, as well, in some cases
automate the treatment of your dois with existing tools such as doiclient or crosscite that allow you e.g. to retrieve a bibtex bibliography of your data
use tools such as pybtex to manage the bibtex conversion to any desired format, including html and markdown
keep a lookout for a data reference management tool that would simplify and streamline these tasks
I am happy to announce that I have started the new design for my website! I look forward to showing you the fresh look and improved usability that the new design will bring. Stay tuned for more updates as the new design becomes available.
The first thing I am implementing is the blog. I am experimenting with the options to customize a nice experience (so I hope at least). You can leave feedback here (using Disqus) if you have suggestions or comments. Read More…
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